Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy

In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impa...

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Autores principales:	Saeed Farzin, Mahdi Valikhan Anaraki
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	climate change flower pollination algorithm hybridization strategy least-squares support-vector machine suspended sediment loads Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/143b271291d349bbbb1fd1925c7efa59
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spelling	oai:doaj.org-article:143b271291d349bbbb1fd1925c7efa592021-11-05T19:07:32ZModeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy2040-22442408-935410.2166/wcc.2021.317https://doaj.org/article/143b271291d349bbbb1fd1925c7efa592021-09-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/6/2422https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impacts of climate change on runoff and suspended sediment load (SSL) in the Lighvan Basin in the period (2020–2099). Also, the results of modeling were compared to those of LS-SVM and adaptive neuro-fuzzy inference system (ANFIS) methods. The comparison of runoff and SSL modeling results showed that the LS-SVM-FPA algorithm had the best results and the ANFIS algorithm had the worst results. After the acceptable performance of the LS-SVM-FPA algorithm was proved, the algorithm was used to predict runoff and SSL under climate change conditions based on ensemble GCM outputs for periods (2020–2034, 2035–2049, 2070–2084, and 2085–2099) under three scenarios of RCP2.6, RCP4.5, and RCP8.5. The results showed a decrease in the runoff in all periods and scenarios, except for the two near periods under the RCP2.6 scenario for runoff. The predicted runoff and SSL time series also showed that the SSL values were lower than the average observation period, except for 2036–2039 (up to an 8% increase in 2038). HIGHLIGHTS A hybrid strategy has been developed for modeling and predicting suspended sediment load (SSL) under climate change.; A hybrid strategy was compared to standalone LS-SVM and adaptive neuro-fuzzy inference system.; This approach has the potential to model and predict various hydrological variables.; The ensemble general circulation model of fifth report and RCP scenarios were employed for predicting SSL.;Saeed FarzinMahdi Valikhan AnarakiIWA Publishingarticleclimate changeflower pollination algorithmhybridization strategyleast-squares support-vector machinesuspended sediment loadsEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 6, Pp 2422-2443 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	climate change flower pollination algorithm hybridization strategy least-squares support-vector machine suspended sediment loads Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	climate change flower pollination algorithm hybridization strategy least-squares support-vector machine suspended sediment loads Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Saeed Farzin Mahdi Valikhan Anaraki Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy
description	In the present study, for the first time, a new strategy based on a combination of the hybrid least-squares support-vector machine (LS-SVM) and flower pollination algorithm (FPA), average 24 general circulation model (GCM) output, and delta change factor method has been developed to achieve the impacts of climate change on runoff and suspended sediment load (SSL) in the Lighvan Basin in the period (2020–2099). Also, the results of modeling were compared to those of LS-SVM and adaptive neuro-fuzzy inference system (ANFIS) methods. The comparison of runoff and SSL modeling results showed that the LS-SVM-FPA algorithm had the best results and the ANFIS algorithm had the worst results. After the acceptable performance of the LS-SVM-FPA algorithm was proved, the algorithm was used to predict runoff and SSL under climate change conditions based on ensemble GCM outputs for periods (2020–2034, 2035–2049, 2070–2084, and 2085–2099) under three scenarios of RCP2.6, RCP4.5, and RCP8.5. The results showed a decrease in the runoff in all periods and scenarios, except for the two near periods under the RCP2.6 scenario for runoff. The predicted runoff and SSL time series also showed that the SSL values were lower than the average observation period, except for 2036–2039 (up to an 8% increase in 2038). HIGHLIGHTS A hybrid strategy has been developed for modeling and predicting suspended sediment load (SSL) under climate change.; A hybrid strategy was compared to standalone LS-SVM and adaptive neuro-fuzzy inference system.; This approach has the potential to model and predict various hydrological variables.; The ensemble general circulation model of fifth report and RCP scenarios were employed for predicting SSL.;
format	article
author	Saeed Farzin Mahdi Valikhan Anaraki
author_facet	Saeed Farzin Mahdi Valikhan Anaraki
author_sort	Saeed Farzin
title	Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy
title_short	Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy
title_full	Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy
title_fullStr	Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy
title_full_unstemmed	Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy
title_sort	modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/143b271291d349bbbb1fd1925c7efa59
work_keys_str_mv	AT saeedfarzin modelingandpredictingsuspendedsedimentloadunderclimatechangeconditionsanewhybridizationstrategy AT mahdivalikhananaraki modelingandpredictingsuspendedsedimentloadunderclimatechangeconditionsanewhybridizationstrategy
_version_	1718444021599174656

Modeling and predicting suspended sediment load under climate change conditions: a new hybridization strategy

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